Gender Bias in Perceived Quality. An Experiment with Elite Soccer Performance
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Department of Business Administration
UZH Business Working Paper Series
Working Paper No. 391
Gender Bias in Perceived Quality. An Experiment with Elite
Soccer Performance
Carlos Gomez-Gonzalez, Helmut Dietl, David Berri, and Cornel
Nesseler
First Draft: 01 November 2021
Current Draft: 20 May 2022
University of Zurich, Plattenstrasse 14, CH-8053 Zurich,
http://www.business.uzh.ch/forschung/wps.html
UZH Business Working Paper Series
Contact Details
Carlos Gomez Gonzalez
University of Zurich
Department of Business Administration
Plattenstrasse 14, CH-8032 Zurich, Switzerland
carlos.gomez@business.uzh.ch
Tel.: +41 44 634 53 14
Gender Bias in Perceived Quality. An Experiment with Elite Soccer Performance
Abstract
Whether one looks at revenue, investment, or coverage, men’s sports do better than women’s.
Many assume that the differences are driven by absolute differences in quality of athletic
performance. However, the existence of stereotypes should alert us to another possibility: What if
perceived quality is filtered through gender stereotypes? We perform an experiment showing
participants video clips of elite female and male soccer players. In the control group, participants
evaluated normal videos where the gender of the players was clear to see. In the treatment group,
participants evaluated the same videos but with gender obscured by blurring. We find that
participants only rated men’s videos higher when they knew they were watching men. When they
didn’t know who they were watching, ratings for female and male athletes did not differ
significantly. The findings are consistent with the interpretation that gender bias plays a role in
the evaluation of athletic performance. Implications for research and the sports industry are
discussed.
Keywords: experiment; evaluation; gender bias; fans; soccer; women’s sport
11. Introduction
Stereotypical judgments and misperceptions influence the slow professional advancement of
women (Ellemers, 2018; Valian, 1999). Research shows that negative gender stereotypes about
women’s performance exist in business (Gupta & Turban, 2012; Heilman, 2001), education
(Boring, 2017; Mengel et al., 2019), politics (Dolan & Sanbonmatsu, 2009; Lawless, 2009),
science (Moss-Racusin et al., 2012; Régner et al., 2019; Witteman et al., 2019), and sports
(Burton, 2015; Gomez-Gonzalez et al., 2019). In male-dominated occupations, women routinely
need to deal with criticism of their talent and hardiness (Leslie et al., 2015; Morganson et al.,
2010).
In sports, the abilities of female athletes are constantly questioned by some fans and
reporters on TV, Twitter, Facebook, and other media outlets (Hyde, 2019; Scheadler & Wagstaff,
2018; Trolan, 2013). Investment and media coverage differences are evident and striking as
women’s sports continue to get very little attention (Cooky et al., 2013; Kassouf, 2020;
Matheson, 2021; UNESCO, 2021). Men’s professional sports leagues outperform their women’s
counterparts in economic terms, including demand figures, broadcasting revenue, franchise value,
and athlete pay (Zimbalist, 2019).1
From a managerial perspective, the question is why this difference exists and what can be
done. Literature on objective and perceived quality offers helpful insights into this question
(Mitra & Golder, 2006). While objective quality of a product is based on measurable attributes,
1
For example, in December 2019 it was announced that David Tepper had paid $325 million for a new Major
League Soccer (MLS) franchise in Charlotte, North Carolina (Negley, 2019). Just a few months later, it was thought
the expansion fee for a new National Women's Soccer League (NWSL) team in Sacramento, California, was between
$1 and $2 million (Kassouf, 2020). That same year, the Seattle Reign—an existing NWSL team—sold to French
investors for less than $4 million. These disparities in private investment are also seen with respect to public
investment (Matheson, 2021).
2perceived quality is based on subjective judgments. It certainly is the case that women’s and
men’s soccer are different and that, for example, female soccer players run slower than male
players (Cheuvront et al., 2005). However, it is debatable that physical differences are what
drives demand. The objective quality of women’s and men’s games is difficult to measure, and
impossible for managers to influence.
Perceived quality, however, has considerable managerial implications. Managers can
influence perception, and perception influences demand (García & Rodríguez, 2002; Pawlowski
et al., 2018). Stakeholders change the perception of quality among sports fans through, for
example, coverage, promotion, and engagement strategies. We argue that those watching men’s
and women’s soccer today have a gender bias that influences the perception of quality. Men’s
soccer appears more often on TV and players get paid more (Cooky et al., 2013; Zimbalist,
2019); therefore, fans tend to believe that the men’s version is better.
We performed an experiment to analyze the influence that gender stereotypes may have
on the perceived quality of women’s and men’s soccer. Participants were shown videos of
professional women’s and men’s soccer players scoring goals. In the control group, videos were
unmodified. In the treatment group, the gender of the players was blurred, making it impossible
for a viewer to know if they were watching men or women. Participants were then asked to
evaluate the overall performance of the players. The results show that when participants could
identify the gender of the players, male players were rated higher. When the gender of the players
could not be ascertained, however, no significant differences between ratings of female and male
athletes were detected. The results have implications for the coverage of women’s sports in the
media and how the performance of female athletes is perceived (Fink, 2015).
3The approach adopted here has been used in other research settings. For example, in the
hiring process, research shows that employers tend to discriminate less when the gender of the
candidate is hidden (Åslund & Skans, 2012; Goldin & Rouse, 2000; Krause et al., 2012). The
concept of blinding identity features to improve decisions has also been explored in science
(Budden et al., 2008; Johnson & Kirk, 2020), education (Hinnerich et al., 2015), and criminal
justice (Sah et al., 2015). To our knowledge, ours is the first study to empirically analyze the
effect of blinded decision-making in the context of gender bias and professional sports.
The paper is structured as follows. Section 2 reviews the literature and presents the
theoretical framework on hegemonic masculinity and gender imprinting. Section 3 describes the
experimental design and method; Section 4 the results. Section 5 discusses implications and
limitations, and concludes the paper.
2. Theoretical framework and literature review
To date, research on women’s sports has developed some key theoretical questions (Cunningham,
2013; Fink, 2013). Specific topics have been studied such as the underrepresentation of women in
leadership roles (Burton, 2015; Darvin et al., 2021; Gomez-Gonzalez et al., 2019), group
diversity and team and organizational outcomes (Lee & Cunningham, 2018; Taylor et al., 2022;
Wicker et al., 2019), and demand and consumption (Funk et al., 2003; Valenti et al., 2020).
Thomson et al. (2022) argue advancing women’s professional sports further may benefit from
multidisciplinary research.
Women’s sports leagues are experiencing remarkable growth in attention and
consumption, supporting athletes competing at the highest level (Litchfield & Kavanagh, 2019;
Pegoraro et al., 2018; Taylor et al., 2020). In 2020, in the US, the National Women’s Soccer
League broke its viewership records by nearly 300% (Gao, 2020). The NCAA Women’s
4Gymnastics Championship and the Women’s National Basketball Association are other North
American leagues with record-breaking viewership figures. In Europe, the trend is similar. FC
Barcelona Femení has recently registered world-record attendance figures in soccer, with crowds
of over 90,000 spectators (Burhan, 2022).
Despite this growth and its business potential, women’s sports have no shortage of
detractors (Hyde, 2019; Scheadler & Wagstaff, 2018; Trolan, 2013). Negative comments target
women’s physical attributes (Burch et al., 2018), sexuality (Cavalier & Newhall, 2018), athletic
skills (Joncheray et al., 2016), and overall value (Micelotta et al., 2018). Even female fans are not
immune to detrimental perceptions (Sveinson et al., 2019). This negative notion about women’s
sports may have an influence on the perception of quality. The negativity surround women’s
sports should, we argue, be understood within a context of hegemonic masculinity (Hindman &
Walker, 2020; Hoeber & Kerwin, 2013) and gender imprinting (Gisladottir & Reid, 2021;
Micelotta et al., 2018).
2.1. Hegemonic masculinity
Hegemonic masculinity refers to practices and beliefs that establish and reinforce men’s control
and status over other groups, including women (Connell & Messerschmidt, 2005). Scholars argue
that stereotypical attributes of men (e.g., strength, competitiveness, and risk preferences) are
overemphasized in sports (Englis, 2017). The lack of these attributes is often cited in social media
critiques of women’s athletics (Hyde, 2019) and as justifications for the underrepresentation of
women in sports leadership positions (Burton, 2015).
In sports management research, scholars have discussed the relationship between
hegemonic masculinity and the role of women in sports at the organizational level (Burton, 2015;
5Hoeber & Kerwin, 2013). For example, Hindman and Walker (2020) argue that gender
stereotypes result in disadvantages for women in hiring, promotion, and evaluation.
Such gender stereotypes require women to deal with criticisms of their talent and strength,
and lead people to believe that men are a better fit for the job. Men are often perceived as more
competent and evaluated better than women with equal qualifications for sport-related positions
(Burton et al., 2012). However, the gender stereotypes that reward masculinity traits go beyond
the organizational culture of specific institutions (LaVoi & Dutove, 2012). They have the power
to influence the public’s perception of women’s performance.
2.2. Gender imprinting
Micelotta et al. (2018) introduced the concept of “gender imprinting” in the context of sports.
Broadly, this concept refers to embedded sociocultural gender attributes that define values,
norms, and beliefs in specific industries. The gender imprinting of the sports industry is rooted in
its origin, when hegemonic masculinity shaped organizational norms, defined the sports culture,
and desired practices (Gisladottir & Reid, 2021; Micelotta et al., 2018). Therefore, gender
imprinting is closely related to the global level of hegemonic masculinity (Connell &
Messerschmidt, 2005).
Although some societies and industries are taking steps to advance gender equity
(Mendoza & Zurka, 2021; United Nations, 2022), gender stereotypes and prejudices are likely to
influence behavior today. For example, Micelotta et al. (2018) show that cultural barriers
negatively affect the capacity of new ventures to acquire symbolic and material support in
women’s sports. The sociocultural level of women’s sports is imprinted with prejudices about
athletes’ abilities, which determine the capacity to compete with the dominant male sports culture
(Scheadler & Wagstaff, 2018).
6Constant exposure to negative comments about women’s attributes, athletic skills, and
overall value—imprinted within the industry and embedded in society—can negatively influence
the views and perceptions of fans (Travers, 2008). We argue that gender stereotypes negatively
influence perceived quality among fans who watch women’s games, highlights, or any other
sport-related content. We hypothesize that most fans would not perceive any differences in
quality between top male and female players—if the gender was not clear to see.
H1. Fans evaluate women’s performance lower only if the gender of the players is clear to see.
3. Method
3.1. Experimental videos
An experiment tested this hypothesis. We selected 10 videos from women’s and men’s club and
national team matches in 2019. The videos were between 5 and 14 seconds each. We used goal-
scoring plays that were chosen by either UEFA or FIFA as highlights for the season. Sports
highlights are an increasing touchpoint to connect with the younger generations, who prefer
short-form content to consume and share on social media platforms (Silverman, 2020). All videos
featured highly skilled players and contained some of the most-watched goals on TV and social
media. Appendix Table A1 provides information about the match, scoring player, and length of
the videos.
To conceal the gender of the players, we manipulated every video frame. Appendix 2
shows the parameters of the manipulated videos in Adobe Premiere Pro. The same mask feather,
opacity, and expansion levels were used for each frame; then the number of blocks (for
blurriness) were selected. The lowest level of blurriness that still allowed us to effectively
conceal the gender of the players was used. We blurred all human-related parts (players and
fans). Although this approach creates noise of its own for the evaluation of the play, pretests
7showed that blurring only critical areas such as players’ heads was not enough to effectively
conceal gender. Figure 1 shows an example of the two versions of the videos, original and
blurred.
[Figure 1 near here]
Two groups were created. In the control group, 290 participants evaluated a sequence of
10 videos in which nothing was blurred (similar to Figure 1, left panel) In the treatment group,
323 participants evaluated the same sequence but with blurred videos (similar to Figure 1, right
panel). Thus, the only difference between groups was the blurriness of players and fans. Each
sequence consisted of five women’s and five men’s soccer videos, presented in random order (see
Appendix Table A2).
3.2. Participants and survey structure
The Amazon Mechanical Turk (AMT) was used to recruit participants. AMT is an online
marketplace in which workers exchange their labor for a monetary reward. AMT results in
heterogeneous samples (e.g., in nationality and age). Outcomes do not significantly differ from
traditional methods (Arechar et al., 2018; Snowberg & Yariv, 2021) and are not affected by
monetary incentives (Snowberg & Yariv, 2021). Employers decide payment and may specify
additional worker qualifications.
In our experiment, the task was to complete a survey estimated to take 5 minutes. There
were no qualification requirements; the pay was $1.00. Relative to most AMT tasks, the
workload was low and the payment high. A sample of 613 participants was recruited (276
women, 337 men; mean age 34 years). The geographic breakdown was Africa (4), Asia (10),
Europe (225), North America (322), Oceania (2), and South America (39). Eleven participants
did not report their nationality.
8Each participant was given a task labeled “Answer a survey about soccer” with the
description “Evaluate soccer performance (about 5 minutes).” We added the keywords survey;
sport; soccer; video. Participants answered questions in survey screens. Before evaluating the
videos, each participant was asked to provide information regarding age, gender, and country.
Participants were also asked to answer the following questions with binary response possibilities:
“Do you watch soccer?”; “Do you watch soccer daily (highlights or matches)?”; and “Do you
prefer men’s or women’s soccer?”
These questions were chosen because people who watch soccer regularly could show
different behavior when evaluating women’s and men’s soccer. Additionally, research suggests
that behavior may be moderately influenced by explicit beliefs and values (Crandall & Eshleman,
2003; Devine et al., 2017). Therefore, we may find a relationship between the preference for
women’s or men’s soccer and the evaluation of the groups, especially when the videos are
unmodified and the gender visible.
In the subsequent screens, after every video, we asked participants (1) to rate the
performance of the players on a 5-point scale: “Overall, the performance of the players is” (1 =
poor; 5 = excellent) and (2) to indicate whether they would pay to watch the match: “Would you
pay to watch the match?” (yes/no). The videos and questions were shown on subsequent survey
screens, and participants had to submit the final task on the last screen. Participants could take up
to 20 minutes to complete the survey and go back and forth among the screens.2
4. Results
Considering first the individual videos, 9 of 10 received a lower rating when they were blurred
(see Figure 2). Three unblurred men’s videos received an average evaluation > 4; none of the
2
In a small subsample, we found no differences when the demographic questions were asked after evaluating the
videos rather than before.
9unblurred women’s videos did so. As expected, blurred videos received lower evaluations (M =
3.52) than the blurred ones (M = 4.22). We expected this result as the blurred videos can create
some noise in perceived quality. Whether blurred or unblurred, the video evaluations show
similar trends. The key point for our research, however, is how participants evaluate men’s and
women’s soccer videos within the two different experimental groups.
[Figure 2 near here]
As shown in Figure 3, when the videos were not blurred and hence the gender visible,
participants rated men’s performance significantly higher than women’s (men’s videos, M =
4.012; women’s videos, M = 3.839; average treatment effect (ATE) = 0.173; Mann-Whitney U, z
= 3.429, p = 0.00, N = 580). However, when the videos were blurred and hence gender not
visible, participants’ ratings did not differ significantly between men’s (M = 3.583) and women’s
(M = 3.556) videos (ATE = 0.027; Mann-Whitney U, z = 0.846, p = 0.398, N = 646).
[Figure 3 near here]
To verify that the results are robust, we controlled for various covariates. Table 1 shows
the regression results with the ratings for unblurred videos as the dependent variable. We
included all control variables stepwise. Model 1 presents only the evaluation of women’s and
men’s videos. Model 2 includes participant demographics. Model 3 includes participants’
preference for women’s or men’s soccer (omitted responses are scored as no preference). Model
4 includes control variables for different sequences and participant country. Most control
variables had no statistically significant influence, although participants who watch soccer daily
made lower ratings overall. Nonetheless, the control variables throughout the models do not
influence the participants’ higher evaluation of men’s videos.
[Table 1 near here]
10Table 2 shows the regression results with the ratings for blurred videos as the dependent
variable. Participants no longer rate men’s and women’s videos differently. This result is
consistent across all models that include the controls. Participants who prefer men’s soccer
evaluated the blurred videos significantly lower. Women evaluated the blurred videos
significantly higher than men. We lost 84 observations in Model 3 as we made a coding error
with the question regarding participants’ preference for male or female soccer. Omitting these
observations in Models 1 and 2 has no significant impact.
[Table 2 near here]
In Appendix Table A3, we tested the interaction between video gender and participant
gender to examine if women and men participants evaluate unblurred videos differently. Men
evaluated female videos significantly lower than male videos, but for women there was no
difference between men’s and women’s videos. Consistent with the results reported in Tables 1
and 2, although smaller in magnitude, when the players’ gender is visible (Table A4), fans report
to be significantly less likely to pay to watch a women’s match; when gender not visible (Table
A5), the effect disappears.
5. Discussion and Conclusions
Many factors contribute to gender disparities in professional advancement (Ellemers, 2018;
Valian, 1999). In many fields, women need to cope with stereotypes that question their ability to
succeed (Leslie et al., 2015; Morganson et al., 2010). Their representation and visibility are
severely limited. For example, in sports, the media seldom cover men and women equally (Cooky
et al., 2013). While 40% of all sports participants are women, women’s sports receive only
around 4% of the total coverage (UNESCO, 2021).
11Some research suggests that preferences drive the type of content displayed by the media
(Gentzkow & Shapiro, 2006). Thus, fans prefer watching male sports because they derive a
higher utility. However, there is reason to suspect that fans are not evaluating men’s and
women’s sports by the same criteria. For example, fans of men’s sports seem quite comfortable
with evaluating male athletes relative to their competition. As we see in boxing, absolute
differences are often not even considered. When comparing men and women, though, absolute
differences are often cited.
The results presented here support the hypothesis that gender stereotypes play a role in
perceived quality. Differences between men’s and women’s performance are perceived only
when the gender of the players is visible. When the gender of the players is no longer
distinguishable, men’s higher ratings disappear. Conceptually, the phenomenon may be summed
up by saying that gender biases “imprinted” in the sports context influence how fans perceive
women’s and men’s professional sports (Micelotta et al., 2018).
This finding is relevant for players, managers, and broadcasters alike. Men’s soccer is
omnipresent throughout societies, through television and social media (Cooky et al., 2013;
Kassouf, 2020; Matheson, 2021; UNESCO, 2021). It is a common assumption that differences in
physiology between men’s and women’s soccer result in recognizable differences in quality.
However, our results show that this assumption must be reevaluated as gender bias may play an
important role. This bias in sports consumption is consistent with discourses of global hegemonic
masculinity (Burton, 2015; Connell & Messerschmidt, 2005; Hindman & Walker, 2020; Hoeber
& Kerwin, 2013) and gender imprinting (Gisladottir & Reid, 2021; Micelotta et al., 2018).
Moderator factors regarding participants’ demographics, preferences, and habits influence
overall evaluation, but do not cancel out the gender bias. For example, participants who watch
12soccer daily rated both blurred and original videos lower than other participants; apparently, the
more you watch soccer, the more it takes to impress you. We did not find the expected influence
of explicit beliefs and values on behavior (Crandall & Eshleman, 2003; Devine et al., 2017).
Participants who express a preference for men’s or women’s soccer do not show a consistent bias
when evaluating the unblurred videos.
5.1. Limitations and Future Research
The findings from the experiment are robust but should be interpreted cautiously. This study has
limitations that suggest opportunities for future research. First, we selected videos that show only
elite performance, which could lead to a ceiling effect. The videos had already been filtered
through the choices of sports media broadcasters and pushed to the top of the list by people who
are not part of our study. Therefore, the focus on popular highlights limits the extent of the
implications. Other videos involving less skilled players could potentially prompt different
evaluations.
Second, we used exclusively goal plays. Modern sports consumption trends show a
growing preference for brief highlights, especially among millennials and Gen Z (Silverman,
2020). However, a soccer match consists of various scenes that can also be interesting for viewers
(e.g., tackles, corners, free kicks, penalty kicks), and many enjoy watching whole matches. Third,
the experimental design includes only 10 videos because of the time needed to modify the videos
to conceal gender (2–6 weeks each). A larger number of videos would ensure higher variability
and allow us to draw more conclusions.
Finally, we cannot rule out the possibility that other variables explain better the observed
relationship. For example, we did not control for the atmosphere in the stadium. Participants
watched the videos without any sound as commentators frequently reveal the gender of the
13player. People often go to the stadium or watch a match not because of the quality but because of
the atmosphere. Future research can also explore other sports and exploit new experimental
designs. Artificial intelligence and machine learning algorithms have the potential to contribute to
this line of research.
5.2. Implications and Concluding Remarks
As societies advance in women’s rights, equity, and equality (Mendoza & Zurka, 2021; United
Nations, 2022), the exposure of future generations to gender stereotypes will decrease. In the
absence of a gender bias, our study show that the perceived quality of men’s and women’s elite
soccer may not differ. This result has managerial implications, as investments in women’s team
sports in terms of money and coverage should become highly attractive.
Perceived quality is a subjective dimension that managers and leagues can deliberately
influence. This dimension is essential for sports consumption because how fans perceive quality
drives demand (García & Rodríguez, 2002; Pawlowski et al., 2018). The current trends in
women’s sports consumption should be sending signals to managers. The perceived quality of
women’s sports is already changing; fans are breaking attendance records and demanding more
coverage of women’s soccer (Burhan, 2022; Gao, 2020).
In theory, online communication forms and new consumption trends should allow more
women’s sports coverage (Kian & Hardin, 2009; Pegoraro et al., 2021). Our results should
persuade sports consumers and managers to be mindful of biases when evaluating men’s and
women’s sports and making decisions about their promotion. Of course, consumers cannot
suddenly change the way they perceive sports, and gender bias will not disappear overnight
(Burton, 2015; LaVoi & Dutove, 2012; Micelotta et al., 2018). Nevertheless, the lack of
differences in perceived quality and the willingness to pay to watch a match are instructive.
14Disclosure statement
The authors report there are no competing interests to declare.
Data availability
The data that support the findings of this study is publicly available in HarvardDataVerse,
https://doi.org/10.7910/DVN/P12G8I (only accessible upon publication). We deleted individual
identifiable information.
Funding details
The author(s) received no financial support for the research, authorship, and/or publication of this
article.
Acknowledgments
For their comments and suggestions, we would like to thank conference participants at the HSE
Analytics for Management and Economics Conference (October, 2020), the 96th WEAI Annual
Conference (June 27‐July 1, 2021), the XI CIED Conference (June 2‐4, 2021), the WEAI
International Conference (March 17‐19, 2021), and seminar participants at the NTNU Spring
Seminar, Center for Sport and Culture Management Research (March, 2021).
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25Figure 1. Example of unblurred videos (L) and blurred videos (R)
Note: a. The left image represents the characteristics of the unblurred condition (the gender of the
players is clear to see). The right image represents the characteristics of the blurred condition (the
gender of the players is concealed). b. These images are not extracted from the videos in the
sample, which omit plays that involve corners, free kicks, penalty kicks, or tackles.
26Figure 2. Evaluation for all videos under the two experimental conditions (average values)
Note: The unblurred condition represents the original videos where the gender of the players is
clear to see (control group). The blurred condition represents the blurred videos where the gender
of the players is concealed (treatment group). The outcome variable is the evaluation of the
overall performance of the players (1 = poor; 5 = excellent).
27Figure 3. Differences in evaluation between female and male soccer videos under the two
experimental conditions (average values)
Note: a. The unblurred condition represents the original videos where the gender of the players is
clear to see (control group). The blurred condition represents the blurred videos where the gender
of the players is concealed (treatment group). b. The outcome variable is the evaluation of the
overall performance of the players (1 = poor; 5 = excellent).
28Table 1. Regression results. Responses to unblurred videos
Dependent variable: Video evaluation
Model 1 Model 2 Model 3 Model 4
Female video -0.17*** -0.17*** -0.17*** -0.17***
(0.054) (0.054) (0.053) (0.053)
Gender of participant (female) 0.048 0.042 0.085
(0.054) (0.054) (0.062)
Age of participant 0.002 0.002 0.002
(0.003) (0.03) (0.003)
Participant watches often 0.23* 0.22
(0.14) (0.14)
Participant watches daily -0.30*** -0.26***
(0.073) (0.073)
Participant has no preference omitted omitted
0.094 0.14
Participant prefers female soccer
(0.090) (0.091)
Participant prefers male soccer -0.056 0.012
(0.11) (0.11)
Sequence control Yes
Continent control Yes
Constant 4.01*** 3.92*** 3.89*** 3.87***
(0.038) (0.11) (0.17) (0.50)
Observations 580 580 580 570
2
Adj. R 0.016 0.017 0.049 0.090
Note: a. The outcome variable is the evaluation of the overall performance of
the players (1 = poor; 5 = excellent).
b. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
c. Model 4 drops 10 observations from participants who did not report their
place of residence.
29Table 2. Regression results. Responses to blurred videos
Dependent variable: Video evaluation
Model 1 Model 2 Model 3 Model 4
Female video -0.027 -0.027 -0.023 -0.018
(0.064) (0.063) (0.064) (0.059)
Gender of participant (female) 0.21*** 0.19*** 0.20***
(0.064) (0.065) (0.066)
Age of participant 0.013*** 0.011*** 0.024
(0.003) (0.003) (0.021)
Participant watches often -0.032 -0.25
(0.18) (0.17)
Participant watches daily -0.25*** 0.023
(0.086) (0.085)
Participant has no preference omitted omitted
-0.10 -0.081
Participant prefers female soccer
(0.10) (0.10)
-0.57*** -0.35**
Participant prefers male soccer
(0.13) (0.14)
Sequence control Yes
Continent control Yes
Constant 3.58*** 3.00*** 3.49*** 3.13***
(0.045) (0.13) (0.24) (0.52)
Observations 646 646 562 550
2
Adj. R 0.000 0.041 0.102 0.214
Note: a. The outcome variable is the evaluation of the overall performance of
the players (1 = poor; 5 = excellent).
b. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
c. Model 3 drops 84 observations due to a coding error for the question about
participants’ preference for male or female soccer. Model 4 drops some
additional observations from participants who did not report their place of
residence. Omitting these observations does not significantly change the
results in Models 1, 2 and 3.
30Appendix 1. Additional information and analyses
Table A1. Videos in the experiment
Video Match Scoring Player Length (in sec) Female
1 Tottenham – Barcelona Ivan Rakitić 14 No
2 Argentina - Croatia Luka Modrić 6 No
3 Liverpool - Bayern Sadio Mané 14 No
4 Manchester City - Raheem Sterling 9 No
5 Russia - Croatia Denis Cheryshev 9 No
Donetsk
6 Chelsea - Lyon Erin Cuthbert 6 Yes
7 Norway – England Lucy Bronze 5 Yes
8 Netherlands – Sweden Jackie Groenen 7 Yes
9 Nigeria – Korea Republic Asisat Oshoala 13 Yes
10 United States - Thailand Alex Morgan 5 Yes
31Table A2. Allocation of female and male videos in subsequent screens (S) across sequences
(Seq.)
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10
Seq. 1 ♂ ♂ ♀ ♂ ♂ ♀ ♀ ♂ ♀ ♀
Seq. 2 ♀ ♀ ♀ ♀ ♂ ♂ ♂ ♂ ♂ ♀
Seq. 3 ♀ ♂ ♀ ♂ ♀ ♂ ♀ ♂ ♀ ♂
Seq. 4 ♀ ♂ ♀ ♂ ♂ ♂ ♀ ♂ ♀ ♀
Seq. 5 ♀ ♂ ♀ ♀ ♂ ♂ ♂ ♀ ♀ ♂
Seq. 6 ♀ ♀ ♀ ♀ ♂ ♂ ♂ ♂ ♂ ♀
Seq. 7 ♂ ♀ ♀ ♀ ♂ ♀ ♂ ♂ ♀ ♂
Notes: ♀ female video; ♂ male video
32Table A3. Regression results. Responses to unblurred videos. Interaction terms
Dependent variable: Video evaluation
Model 1 Model 2 Model 3 Model 4
Male video # Male participant omitted omitted omitted omitted
Male video # Female participant 0.0059 -0.015 -0.000 0.048
(0.077) (0.077) (0.076) (0.082)
Female video # Male participant -0.21*** -0.21*** -0.21*** -0.21***
(0.076) (0.076) (0.075) (0.074)
Female video # Female participant -0.12 -0.14* -0.13* -0.087
(0.077) (0.077) (0.076) (0.082)
Age of participant 0.002 0.002 0.001
(0.003) (0.003) (0.003)
Participant watches often 0.23* 0.22
(0.14) (0.14)
Participant watches daily -0.30*** -0.26***
(0.073) (0.073)
Participant has no preference omitted omitted omitted
Participant prefers female soccer 0.041 0.094 0.14
(0.090) (0.090) (0.091)
Participant prefers male soccer -0.15 -0.056 0.012
(0.11) (0.11) (0.11)
Sequence control Yes
Continent control Yes
Constant 4.01*** 3.96*** 3.91*** 3.89***
(0.054) (0.139) (0.172) (0.50)
Observations 580 580 580 570
2
Adj. R 0.015 0.021 0.046 0.087
Note: a. The outcome variable is the evaluation of the overall performance of the
players (1 = poor; 5 = excellent).
b. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
c. Model 4 drops 10 observations from participants who did not report their place
of residence.
33Table A4. Regression results. Responses to unblurred videos. Willingness to pay
Dependent variable: Willingness to pay
Model 1 Model 2 Model 3 Model 4
Female video -0.07** -0.07** -0.07** -0.07**
(0.029) (0.029) (0.027) (0.027)
Gender of participant (female) 0.049* 0.048* 0.016
(0.029) (0.027) (0.032)
-
Age of participant -0.003** -0.004**
0.005***
(0.002) (0.002) (0.002)
Participant watches often 0.16** 0.19***
(0.07) (0.07)
Participant watches daily 0.24*** 0.22***
(0.036) (0.037)
Participant has no preference omitted omitted
0.10** 0.085*
Participant prefers female soccer
(0.045) (0.046)
Participant prefers male soccer 0.11** 0.075
(0.054) (0.057)
Sequence control Yes
Continent control Yes
Constant 0.79*** 0.87*** 0.47*** 0.34***
(0.020) (0.059) (0.085) (0.25)
Observations 579 579 579 569
2
Adj. R 0.009 0.021 0.158 0.178
Note: a. The outcome variable is if a respondent would be willing to pay to
see the whole match (0 or 1).
b. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
c. Model 4 drops 10 observations from participants who did not report their
place of residence.
34Table A5. Regression results. Responses to blurred videos. Willingness to pay
Dependent variable: Willingness to pay
Model 1 Model 2 Model 3 Model 4
Female video -0.006 -0.006 -0.001 -0.001
(0.029) (0.029) (0.030) (0.030)
Gender of participant (female) -0.027 -0.032 -0.042
(0.030) (0.031) (0.033)
Age of participant 0.000 0.003** -0.012
(0.002) (0.002) (0.011)
Participant watches often 0.25*** 0.27***
(0.087) (0.087)
Participant watches daily 0.15*** 0.15***
(0.041) (0.043)
Participant has no preference omitted omitted
0.10** 0.13**
Participant prefers female soccer
(0.048) (0.052)
0.064 0.091
Participant prefers male soccer
(0.062) (0.070)
Sequence control Yes
Continent control Yes
Constant 0.69*** 0.70*** 0.17 0.24
(0.021) (0.059) (0.11) (0.26)
Observations 646 646 562 550
Adj. R2 0.000 0.001 0.066 0.116
Note: a. The outcome variable is if a respondent would be willing to pay to
see the whole match (0 or 1).
b. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
c. Model 3 drops 84 observations due to a coding error for the question about
participants’ preference for male or female soccer. Model 4 drops some
additional observations from participants who did not report their place of
residence. Omitting these observations does not significantly change the
results in Models 1, 2, and 3.
35Appendix 2. Parameters of the manipulated videos in Adobe Premiere Pro
Mosaic function parameters:
- Feature details:
o Stands and Fans (Surroundings):
• Mask feather: 10.0; Mask Opacity: 100.0 %; Mask Expansion: 0.0
– 10.0
• Horizontal Blocks: 10; Vertical Blocks: 10
• Sharp Colors: No
o Players:
• Mask feather: 10.0; Mask Opacity: 100.0 %; Mask Expansion: 0.0
– 10.0
• Horizontal Blocks: 100; Vertical Blocks: 100
• Sharp Colors: Yes
Lumetri Color function parameters:
- Feature details:
o “Black” Team:
• HSL Secondary
• Key
▪ “Set Color” and “Add Color” with pipette in Video (select surroundings of
players); Invert Mask: Yes
▪ Correction:
▪
▪ Temperature: -100.0
▪ Tint: 100.0
▪ Contrast: 100.0
▪ Sharpen: 0.0
▪ Saturation: 0.0
o “White” Team:
• HSL Secondary
• Key
▪ “Set Color” and “Add Color” with pipette in Video (select surroundings of
Players); Invert Mask: Yes
36▪ Correction:
▪
▪ Temperature: 100.0
▪ Tint: -100.0
▪ Contrast: 100.0
▪ Sharpen: 0.0
▪ Saturation: 0.0
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